Systems, methods, and devices for plaque analysis, vessel and fluid flow analysis, and/or risk determination or prediction thereof

Abstract

This disclosure relates to systems, methods, and devices for plaque analysis, vessel and fluid flow analysis, and/or risk determination or prediction thereof. Some embodiments relate to determining fractional flow reserve (FFR) values. Some embodiments relate to determining FFR values using 3D-printed models. Some embodiments relate to identifying thin cap fibroatheroma based on analysis of computed tomography (CT) imaging. Some embodiments relate adjusting calcified plaque thresholds for CT images to address effects of calcium blooming.

1 . A computer-implemented method for estimating fractional flow reserve for a subject based on image analysis of a medical image of the subject, the computer-implemented method comprising:
 accessing, by a computer system, the medical image of the subject, wherein the medical image of the subject depicts one or more regions of one or more coronary arteries of the subject;   analyzing, by the computer system, the medical image of the subject to identify the one or more regions of the one or more coronary arteries;   determining, by the computer system, vessel geometry of the identified one or more regions of the one or more coronary arteries, wherein the vessel geometry is determined based at least in part on a lumen wall and a vessel wall of the identified one or more regions of the one or more coronary arteries;   generating an input for a machine learning model for determining a fluid flow characteristic, the input based at least in part on the determined vessel geometry;   determining, by the computer system using the machine learning model, the fluid flow characteristic of the identified one or more regions of the one or more coronary arteries, wherein the machine learning model is trained to determine the fluid flow characteristic using fluid flow measurements collected from a plurality of three-dimensional (3D) printed models of coronary arteries of a plurality of sample subjects, and wherein the machine learning model is trained to determine the fluid flow characteristic by:
 for each sample medical image of a plurality of sample medical images associated with a plurality of sample subjects: 
 accessing the sample medical image; 
 analyzing the sample medical image to identify a plurality of vessels depicted in the sample medical image; 
 determining a geometry of each vessel of the plurality of vessels; 
 generating a 3D-printable model of the plurality of vessels, wherein the 3D-printable model comprises a file stored in a non-transitory computer-readable medium; and 
 accessing pressure measurements collected from a physical 3D model of the plurality of vessels produced by a 3D printer using the 3D-printable model,
 wherein the pressure measurements are collected by positioning a pressure sensor in a lumen of the physical 3D model at a plurality of locations within the physical 3D model, 
 wherein the pressure measurements are collected at a plurality of inlet pressures, and 
 wherein the pressure measurements are collected at a plurality of fluid flow rates; and 
 
 providing the determined geometries and the pressure measurements to the machine learning model, wherein the determined geometries are used as inputs, and wherein the determined pressure measurements are used as outputs, 
 wherein the machine learning model is trained to output a pressure pullback gradient curve; 
   determining, by the computer system using the determined fluid flow characteristic of the identified one or more regions of the one or more coronary arteries, one or more fractional flow reserve values associated with one or more locations within the one or more regions of the one or more coronary arteries of the subject,   wherein the computer system comprises a computer processor and an electronic storage medium.   
     
     
         2 . A computer-implemented method for estimating fractional flow reserve for a subject based on image analysis of a medical image of the subject, the computer-implemented method comprising:
 accessing, by a computer system, the medical image of the subject, wherein the medical image of the subject depicts one or more regions of one or more coronary arteries of the subject;   analyzing, by the computer system, the medical image of the subject to identify the one or more regions of the one or more coronary arteries;   determining, by the computer system, vessel geometry of the identified one or more regions of the one or more coronary arteries, wherein the vessel geometry is determined based at least in part on a lumen wall and a vessel wall of the identified one or more regions of the one or more coronary arteries;   generating an input for a machine learning model for determining a fluid flow characteristic, the input based at least in part on the determined vessel geometry;   determining, by the computer system using the machine learning model, the fluid flow characteristic of the identified one or more regions of the one or more coronary arteries, wherein the machine learning model is trained to determine the fluid flow characteristic using fluid flow measurements collected from a plurality of three-dimensional (3D) printed models of coronary arteries of a plurality of sample subjects; and   determining, by the computer system using the determined fluid flow characteristic of the identified one or more regions of the one or more coronary arteries, one or more fractional flow reserve values associated with one or more locations within the one or more regions of the one or more coronary arteries of the subject,   wherein the computer system comprises a computer processor and an electronic storage medium.   
     
     
         3 . The computer-implemented method of  claim 2 , wherein the machine learning model is trained to determine the fluid flow characteristic by:
 for each sample medical image of a plurality of sample medical images associated with a plurality of sample subjects:
 accessing the sample medical image; 
 analyzing the sample medical image to identify a plurality of vessels depicted in the sample medical image; 
 determining a geometry of each vessel of the plurality of vessels; 
 generating a 3D-printable model of the plurality of vessels, wherein the 3D-printable model comprises a file stored in a non-transitory computer-readable medium; and 
   accessing pressure measurements collected from a physical 3D model of the plurality of vessels produced by a 3D printer using the 3D-printable model,
 wherein the pressure measurements are collected by positioning a pressure sensor in a lumen of the physical 3D model at a plurality of locations within the physical 3D model, 
 wherein the pressure measurements are collected at a plurality of inlet pressures, and 
 wherein the pressure measurements are collected at a plurality of fluid flow rates; and 
   providing the determined geometries and the pressure measurements to the machine learning model, wherein the determined geometries are used as inputs and wherein the determined pressure measurements are used as outputs,   wherein the machine learning model is trained to output the pressure measurements.   
     
     
         4 . The computer-implemented method of  claim 2 , wherein the machine learning model is trained to output a pressure pullback gradient curve. 
     
     
         5 . The computer-implemented method of  claim 2 , wherein the input includes information about a region of plaque detected in the one or regions of the one or more coronary arteries of the subject, wherein the information about the region of plaque includes one or more of: plaque length, plaque area, plaque volume, or plaque density. 
     
     
         6 . The computer-implemented method of  claim 5 , wherein the plaque density is one or more of: low density non-calcified plaque, non-calcified plaque, or calcified plaque. 
     
     
         7 . The computer-implemented method of  claim 6 , wherein the medical image is a coronary computed tomography angiography image,
 wherein low density non-calcified plaque corresponds to a radiodensity of between about −189 Hounsfield units (HU) and about 30 HU,   wherein non-calcified plaque corresponds to a radiodensity of between about 31 HU and about 350 HU, and   wherein calcified plaque corresponds to a radiodensity of between about 351 HU and about 2500 HU.   
     
     
         8 . The computer-implemented method of  claim 2 , wherein the medical image is a coronary computed tomography angiography image. 
     
     
         9 . The computer-implemented method of  claim 2 , wherein the wherein the one or more coronary arteries comprise one or more of left main (LM), ramus  intermedius  (RI), left anterior descending (LA D), diagonal one (D1), diagonal two (D2), left circumflex (Cx), obtuse marginal one (OM1), obtuse marginal two (OM2), left posterior descending (L-PDA), left posterolateral branch (L-PLB), right coronary (RCA), right posterior descending (R-PDA), or right posterolateral branch (R-PLB). 
     
     
         10 . The computer-implemented method of  claim 3 , wherein at least one physical 3D model is printed using a plurality of materials, wherein at least one material of the plurality of materials is selected based on a density of a region of plaque identified in the sample medical image. 
     
     
         11 . The computer-implemented method of  claim 10 , wherein the density of the region of plaque is determined based on a radiodensity of the region of plaque in the sample medical image, wherein the density is at least one of: low density non-calcified plaque, non-calcified plaque, or calcified plaque. 
     
     
         12 . The computer-implemented method of  claim 2 , wherein a fractional flow reserve value of the one or more fractional flow reserve values is determined as a ratio of pressure di stal to a region of plaque and pressure proximal to the region of plaque. 
     
     
         13 . The computer-implemented method of  claim 2 , wherein the one or more fractional flow reserve values is determined for at least one of: a vessel, a vessel segment, or a vessel unit length. 
     
     
         14 . A system for estimating fractional flow reserve for a subject based on image analysis of a medical image of the subject, the system comprising:
 at least one processor; and   a computer-readable medium storing instructions that, when executed by the system, cause the system to:
 access the medical image of the subject, wherein the medical image of the subject depicts one or more regions of one or more coronary arteries of the subject; 
 analyze the medical image of the subject to identify the one or more regions of the one or more coronary arteries; 
 determine vessel geometry of the identified one or more regions of the one or more coronary arteries, wherein the vessel geometry is determined based at least in part on a lumen wall and a vessel wall of the identified one or more regions of the one or more coronary arteries; 
 generate an input for a machine learning model for determining a fluid flow characteristic, the input based at least in part on the determined vessel geometry; 
 determine, using the machine learning model, the fluid flow characteristic of the identified one or more regions of the one or more coronary arteries, wherein the machine learning model is trained to determine the fluid flow characteristic using fluid flow measurements collected from a plurality of three-dimensional (3D) printed models of coronary arteries of a plurality of sample subjects; and 
 determine, using the determined fluid flow characteristic of the identified one or more regions of the one or more coronary arteries, one or more fractional flow reserve values associated with one or more locations within the one or more regions of the one or more coronary arteries of the subject. 
   
     
     
         15 . The system of  claim 14 , wherein the machine learning model is trained to determine the fluid flow characteristic by:
 for each sample medical image of a plurality of sample medical images associated with a plurality of sample subjects:
 accessing the sample medical image; 
 analyzing the sample medical image to identify a plurality of vessels depicted in the sample medical image; 
 determining a geometry of each vessel of the plurality of vessels; 
 generating a 3D-printable model of the plurality of vessels, wherein the 3D-printable model comprises a file stored in a non-transitory computer-readable medium; and 
 accessing pressure measurements collected from a physical 3D model of the plurality of vessels produced by a 3D printer using the 3D-printable model,
 wherein the pressure measurements are collected by positioning a pressure sensor in a lumen of the physical 3D model at a plurality of locations within the physical 3D model, 
 wherein the pressure measurements are collected at a plurality of inlet pressures, and 
 wherein the pressure measurements are collected at a plurality of fluid flow rates; 
 
 providing the determined geometries and the pressure measurements to the machine learning model, wherein the determined geometries are used as inputs and wherein the determined pressure measurements are used as outputs, 
 wherein the machine learning model is trained to output the pressure measurements. 
   
     
     
         16 . The system of  claim 14 , wherein the machine learning model is trained to output a pressure pullback gradient curve. 
     
     
         17 . The system of  claim 14 , wherein the input includes information about a region of plaque detected in the one or regions of the one or more coronary arteries of the subject, wherein the information about the region of plaque includes one or more of: plaque length, plaque area, plaque volume, or plaque density. 
     
     
         18 . The system of  claim 17 , wherein the plaque density is one or more of: low density non-calcified plaque, non-calcified plaque, or calcified plaque. 
     
     
         19 . The system of  claim 18 , wherein the medical image is a coronary computed tomography angiography image,
 wherein low density non-calcified plaque corresponds to a radiodensity of between about −189 Hounsfield units (HU) and about 30 HU,   wherein non-calcified plaque corresponds to a radiodensity of between about 31 HU and about 350 HU, and   wherein calcified plaque corresponds to a radiodensity of between about 351 HU and about 2500 HU.   
     
     
         20 . The system of  claim 14 , wherein the medical image is a coronary computed tomography angiography image. 
     
     
         21 . The system of  claim 14 , wherein the wherein the one or more coronary arteries comprise one or more of left main (LM), ramus  intermedius  (RI), left anterior descending (LAD), diagonal one (D1), diagonal two (D2), left circumflex (Cx), obtuse marginal one (OM1), obtuse marginal two (OM2), left posterior descending (L-PDA), left posterolateral branch (L-PLB), right coronary (RCA), right posterior descending (R-PDA), or right posterolateral branch (R-PLB). 
     
     
         22 . The system of  claim 15 , wherein at least one physical 3D model is printed using a plurality of materials, wherein at least one material of the plurality of materials is selected based on a density of a region of plaque identified in the sample medical image. 
     
     
         23 . The system of  claim 22 , wherein the density of the region of plaque is determined based on a radiodensity of the region of plaque in the sample medical image, wherein the density is at least one of: low density non-calcified plaque, non-calcified plaque, or calcified plaque. 
     
     
         24 . The system of  claim 14 , wherein a fractional flow reserve value of the one or more fractional flow reserve values is determined as a ratio of pressure distal to a region of plaque and pressure proximal to the region of plaque. 
     
     
         25 . The system of  claim 14 , wherein the one or more fractional flow reserve values is determined for at least one of: a vessel, a vessel segment, or a vessel unit length.